index
int32
0
499
hashtag
stringlengths
4
42
segmentation
stringlengths
4
48
0
tryingtosleep
trying to sleep
1
mixture
mixture
2
runit
run it
3
whatadream
what a dream
4
keepitcoming
keep it coming
5
originalman
original man
6
sportsnews
sports news
7
onrepeat
on repeat
8
creative
creative
9
thegrandmaster
the grandmaster
10
nicee
nicee
11
photocontest
photo contest
12
linux
linux
13
exactwords
exact words
14
cursing
cursing
15
degeneratemedia
degenerate media
16
powercouple
power couple
17
factsoflife
facts of life
18
badatreferences
bad at references
19
awardshow
award show
20
cyclists
cyclists
21
season1episode2
season 1 episode 2
22
boxervsrain
boxer vs rain
23
lovewithouttragedy
love without tragedy
24
inmyopinion
in my opinion
25
thatsmydaddy
thats my daddy
26
whatelseisnew
what else is new
27
privateschoolprobz
private school probz
28
Deleanorrie
Deleanorrie
29
Batflack
Batflack
30
Mikey
Mikey
31
Copped
Copped
32
NextMicrosoftCEO
Next Microsoft CEO
33
longlive
long live
34
usairways
us airways
35
Hoodrat
Hood rat
36
FINALLY
FINALLY
37
heronrine
heronrine
38
TheDuo
The Duo
39
heartless
heartless
40
CastleonTNT
Castle on TNT
41
Espana
Espana
42
YouHitItFirst
You Hit It First
43
noideawhy
no idea why
44
7thSeptember
7 th September
45
SyawalJuga
Syawal Juga
46
RootyQ
Rooty Q
47
corn
corn
48
dingdingding
ding ding ding
49
BeatLA
Beat LA
50
ThatsAttractive
Thats Attractive
51
syrian
syrian
52
AmWriting
Am Writing
53
Have
Have
54
NSAplz
NSA plz
55
emblem3
emblem3
56
WEATHERDELAYS
WEATHER DELAYS
57
booktrailer
book trailer
58
DailyFantasy
Daily Fantasy
59
21ReasonsWhyILoveDemi
21 Reasons Why I Love Demi
60
WhenSnoopHostsBETHipHopAwards
When Snoop Hosts BET Hip Hop Awards
61
NewYorkGiants
New York Giants
62
keeppounding
keep pounding
63
Reporter
Reporter
64
Hillary2016
Hillary 2016
65
Falcons
Falcons
66
RobertDeNiro
Robert De Niro
67
Auspol
Auspol
68
Etsy
Etsy
69
LakeHouse
Lake House
70
everyoneknowsthis
everyone knows this
71
youcandefinitelyhearusroar
you can definitely hear us roar
72
Lumens
Lumens
73
MonaNelsonTrial
Mona Nelson Trial
74
DUCKFACE
DUCKFACE
75
TheFinalCut
The Final Cut
76
WhatALadYouAre
What A Lad You Are
77
AlwaysOn
Always On
78
sorrygirls
sorry girls
79
ThinkOutOfTgeBox
Think Out Of Tge Box
80
BetterCallSaul
Better Call Saul
81
TheFreshPrinceOfBelAir
The Fresh Prince Of Bel Air
82
GetDunkedon
Get Dunked on
83
waldoiscool
waldo is cool
84
joemacintosh
joe macintosh
85
pandoraonpointtho
pandora onpoint tho
86
workisdeath
work is death
87
jaimelovesstuff
jaime loves stuff
88
SmallBusiness
Small Business
89
AriantorsAreWorried
Ariantors Are Worried
90
Doggie
Doggie
91
ANTMBritshInvasion
ANTM Britsh Invasion
92
nano
nano
93
RosebarDayParty
Rosebar Day Party
94
willpower
willpower
95
favoritebook
favorite book
96
NoDaysOff
No Days Off
97
royalclubentertainment
royal club entertainment
98
Bruce2Wpg
Bruce 2 Wpg
99
GimmeGimmeGimme
Gimme Gimme Gimme
YAML Metadata Warning: The task_categories "structure-prediction" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other

Dataset Card for BOUN

Dataset Summary

Dev-BOUN is a Development set that includes 500 manually segmented hashtags. These are selected from tweets about movies, tv shows, popular people, sports teams etc.

Test-BOUN is a Test set that includes 500 manually segmented hashtags. These are selected from tweets about movies, tv shows, popular people, sports teams etc.

Languages

English

Dataset Structure

Data Instances

{
    "index": 0,
    "hashtag": "tryingtosleep",
    "segmentation": "trying to sleep"
}

Data Fields

  • index: a numerical index.
  • hashtag: the original hashtag.
  • segmentation: the gold segmentation for the hashtag.

Dataset Creation

  • All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: hashtag and segmentation or identifier and segmentation.

  • The only difference between hashtag and segmentation or between identifier and segmentation are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields.

  • There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as _ , :, ~ ).

  • If there are any annotations for named entity recognition and other token classification tasks, they are given in a spans field.

Additional Information

Citation Information

@article{celebi2018segmenting,
  title={Segmenting hashtags and analyzing their grammatical structure},
  author={Celebi, Arda and {\"O}zg{\"u}r, Arzucan},
  journal={Journal of the Association for Information Science and Technology},
  volume={69},
  number={5},
  pages={675--686},
  year={2018},
  publisher={Wiley Online Library}
}

Contributions

This dataset was added by @ruanchaves while developing the hashformers library.

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